Oncogenic pathway combinations predict clinical prognosis in gastric cancer.

Many solid cancers are known to exhibit a high degree of heterogeneity in their deregulationof different oncogenicpathways. We sought to identify major oncogenicpathways in gastric cancer (GC) with significant relationships to patient survival. Using gene expression signatures, we devised an in silico strategy to map patterns of oncogenicpathway activation in 301 primary gastric cancers, the second highest cause of global cancer mortality. We identified three oncogenicpathways (proliferation/stem cell, NF-κB, and Wnt/β-catenin) deregulatedin the majority (>70%) of gastric cancers. We functionally validated these pathway predictions in a panel of gastric cancer cell lines. Patient stratification by oncogenicpathway combinations showed reproducible and significant survival differences in multiple cohorts, suggesting that pathway interactions may play an important role in influencing disease behavior. Individual GCs can be successfully taxonomized by oncogenicpathway activity into biologically and clinically relevant subgroups. Predicting pathway activity by expression signatures thus permits the study of multiple cancer-related pathways interacting simultaneously in primary cancers, at a scale not currently achievable by other platforms.